2 citations · 2 across the 9 of their papers we have counts for
7 papers · 1 filter
Pillar-0: A New Frontier for Radiology Foundation Models
Kumar Krishna Agrawal, Longchao Liu, Long Lian +11
Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full sp…
GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation
Karim Elmaaroufi, Liheng Lai, Justin Svegliato +3
Vision Language Models (VLMs) achieve strong performance on many vision-language tasks but often struggle with spatial reasoning$\unicode{x2014}$a prerequisite for many application…
Transformers Discover Molecular Structure Without Graph Priors
Tobias Kreiman, Yutong Bai, Fadi Atieh +3
Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to traditional physics-based modeling. Howev…
The Serial Scaling Hypothesis
Yuxi Liu, Konpat Preechakul, Kananart Kuwaranancharoen +1
While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential. These "inherently serial" proble…
Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar +3
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic po…
REOrdering Patches Improves Vision Models
Declan Kutscher, David M. Chan, Yutong Bai +2
Sequence models such as transformers require inputs to be represented as one-dimensional sequences. In vision, this typically involves flattening images using a fixed row-major (ra…
Vector Quantized Feature Fields for Fast 3D Semantic Lifting
George Tang, Aditya Agarwal, Weiqiao Han +2
We generalize lifting to semantic lifting by incorporating per-view masks that indicate relevant pixels for lifting tasks. These masks are determined by querying corresponding mult…